Evidence map›Paper›PMID 36770009›Full record

ArticleMaterials (Basel, Switzerland)2023

Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence.

Stefan Höving, Laura Neuendorf, Timo Betting, Norbert Kockmann

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stefan HövingLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, Germany.ORCID 0000-0002-9083-3318
Laura NeuendorfLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, Germany.ORCID 0000-0001-6593-1752
Timo BettingLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, Germany.ORCID 0000-0003-2538-8059
Norbert KockmannLaboratory of Equipment Design, Department of Biochemical and Chemical Engineering, TU Dortmund University, Emil-Figge-Straße 68, 44227 Dortmund, Germany.ORCID 0000-0002-2367-1988

Funding

German Federal Ministry of Economic Affairs and Climate Action 01MK20014SGerman Federal Ministry of Economic Affairs and Climate Action 03ET1652F
6 · The paper itself

Abstract

The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented.

Indexed as

determination of particle size distributionsMask–RCNNmicro-computed tomographythree dimensional particle analysis in bulkWadell sphericity

Identifiers

PMID36770009
PMCPMC9920040

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.